Electrical deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective for ameliorating the motor symptoms of Parkinson’s disease (PD) including bradykinesia. The STN receives its main excitatory input from cortex; however, the contribution of cortico-subthalamic projection neurons to the effects of DBS remains unclear. To isolate the consequences of stimulating layer 5 primary motor cortex (M1) projections to the STN, we used a dual virus transfection technique to selectively express opsins in these neurons in mice made parkinsonian by unilateral nigrostriatal 6-OHDA lesioning. AAVs containing WGA-Cre constructs were injected in the STN to retrogradely place Cre in STN afferents, while AAVs containing Cre-dependent ultrafast hChR2(E123T/T159C)-EYFP opsin constructs were injected in M1 layer 5, producing specific opsin expression in M1-STN projections. Under unstimulated conditions, lesioned mice showed bradykinesia and hypokinesia (decreased movement), along with electrophysiological changes similar to those observed in PD patients. Specifically, low frequency power (theta, alpha, low beta) was increased and gamma power was decreased, while M1/STN coherence and STN phase-amplitude-coupling (PAC) were increased. Optogenetic stimulation (100–130 Hz) of STN afferents in these mice ameliorated bradykinesia and hypokinesia and brought the neural dynamics closer to the non-parkinsonian state by reducing theta and alpha and increasing gamma power in M1, decreasing STN PAC, and reducing theta band coherence. Histological examination of the EYFP expression revealed that, in addition to orthodromic and antidromic effects, stimulation of cortico-subthalamic neurons may cause wide-spread increased glutamatergic activity due to collaterals that project to areas of the thalamus and other brain regions.

Vagus nerve stimulation (VNS) has been shown to enhance learning and memory, yet the mechanisms behind these enhancements are unknown. Here, we present evidence that epigenetic modulation underlies VNS-induced improvements in cognition. We show that VNS enhances novelty preference (NP); alters the hippocampal, cortical, and blood epigenetic transcriptomes; and epigenetically modulates neuronal plasticity and stress-response signaling genes in male Sprague Dawley rats. Brain-behavior analysis revealed structure-specific relationships between NP test performance (NPTP) and epigenetic alterations. In the hippocampus, NPTP correlated with decreased histone deacetylase 11 (HDAC11), a transcriptional repressor enriched in CA1 cells important for memory consolidation. In the cortex, the immediate early gene (IEG) ARC was increased in VNS rats and correlated with transcription of plasticity genes and epigenetic regulators, including HDAC3. For rats engaged in NPTP, ARC correlated with performance. Interestingly, blood ARC transcripts decreased in VNS rats performing NPTP, but increased in VNS-only rats. Because DNA double-strand breaks (DSBs) facilitate transcription of IEGs, we investigated phosphorylated H2A.X (␥H2A.X), a histone modification known to colocalize with DSBs. In agreement with reduced cortical stress-response transcription factor NF-B1, chromatin immunoprecipitation revealed reduced ␥H2A.X in the ARC promoter. Surprisingly, VNS did not significantly reduce transcription of cortical or hippocampal proinflammatory cytokines. However, TNFRSF11B (osteoprotegerin) correlated with NPTP as well as plasticity, stress-response signaling, and epigenetic regulation transcripts in both hippocampus and cortex. Together, our findings provide the first evidence that VNS induces widespread changes in the cognitive epigenetic landscape and specifically affects epigenetic modulators associated with NPTP, stress-response signaling, memory consolidation, and cortical neural remodeling.

Parkinson's disease is known to be associated with abnormal electrical spiking activities of basal ganglia neurons, including changes in firing rate, bursting activities and oscillatory firing patterns and changes in entropy. We explored the relative importance of these measures through optimal feature selection and discrimination analysis methods. We identified key characteristics of basal ganglia activity that predicted whether the neurons were recorded in the normal or parkinsonian state. Starting with 29 features extracted from the spike timing of neurons recorded in normal and parkinsonian monkeys in the internal or external segment of the globus pallidus or the subthalamic nucleus (STN), we used a method that incorporates a support vector machine algorithm to find feature combinations that optimally discriminate between the normal and parkinsonian states. Our results demonstrate that the discrimination power of combinations of specific features is higher than that of single features, or of all features combined, and that the most discriminative feature sets differ substantially between basal ganglia structures. Each nucleus or class of neurons in the basal ganglia may react differently to the parkinsonian condition, and the features used to describe this state should be adapted to the neuron type under study. The feature that was overall most predictive of the parkinsonian state in our data set was a high STN intraburst frequency. Interestingly, this feature was not correlated with parameters describing oscillatory firing properties in recordings made in the normal condition but was significantly correlated with spectral power in specific frequency bands in recordings from the parkinsonian state (specifically with power in the 8-13 Hz band).

In this study, modulation index (MI) features derived from local field potential (LFP) recordings in the subthalamic nucleus (STN) and electroencephalographic recordings (EEGs) from the primary motor cortex are shown to correlate with both the overall motor impairment and motor subscores in a monkey model of parkinsonism. The MI features used are measures of phase-amplitude cross frequency coupling (CFC) between frequency sub-bands. We used complex wavelet transforms to extract six spectral sub-bands within the 3–60 Hz range from LFP and EEG signals. Using the method of canonical correlation, we show that weighted combinations of the MI features in LFP or EEG signals correlate significantly with individual and composite scores on a scale for parkinsonian disability.